Finding Plans with Branches, Loops, and Preconditions

We present a new approach for finding conditional plans with loops and branches for planning in situations with uncertainty in state properties as well as in object quantities. We use a state abstraction technique from static analysis of programs to builds such plans incrementally using generalizations of input example plans generated by classical planners. Preconditions of the resulting plans with loops are computed by analyzing the changes in the counts of objects of different types across each loop. The scope and scalability of this approach are demonstrated using experimental results on common benchmark domains.